Why Putting Employees First and Customers Second Works

GUEST POST from David Burkus

What if your company announced that, moving forward, it would be place customers second on its list of priorities?

Sounds crazy. The customer is always right. Surely the customer is always first as well.

But that’s exactly what Vineet Nayar, CEO of HCL Technologies did over a decade ago. He announced that the company’s senior leaders would be placing the needs of employees first, and customers second. And the results have been spectacular.

How The Employees First Strategy Started

In 2006, Vineet Nayar, CEO of HCL, a digital engineering company based in India, boldly told his clients they were no longer the company’s top priority. Instead, the focus would be put on employees first. His belief was simple: happy employees make happy customers. Nayar labeled employees who actually interacted with customers as the “value zone,” where the real business magic happens — and any employee in the value zone received the dedicated focus of managers and support functions.

To bring this to life, he flipped the traditional management structure. He made the organizational chart look like an upside-down pyramid. Turning the hierarchy upside down required making managers accountable to front-line employees and ensuring that those in the support functions actually supported those front-line employees, instead of just insisting that they follow the hierarchy’s rigid systems.

Nayar focused his attention on two areas to ensure that the management and support functions served the front-line: reversing accountability and building transparency. Specifically, 360-degree feedback evaluations were expanded to include more front-line workers’ feedback for managers and senior level executives (that’s the accountability), and crucially those evaluations were made public so everyone who contributed to the survey could see the results (there’s your transparency). In addition, when problems occurred for front-line workers, they could create and own support tickets that their managers would have to address (usually, it’s the other way around in top to bottom organizations).

It’s important to note that HCL Technologies wasn’t a little start up in a garage or even a 50-person company. This was done at a 55,000 person, multinational organization. And, spoiler alert, it’s now grown to over 200,000 employees. Pulling off this flip was no small feat, but the results speak for themselves. Employee satisfaction soared, customer service improved, and revenues nearly tripled. By 2009, HCL was named India’s best employer.

Contrast this story with an example of what can go wrong when employee experience is overlooked. In 2001, Robert Nardelli was the newly minted CEO of Home Depot. Expectations were high given his track record at his old job at General Electric, where he had led several successful manufacturing operations.

At Home Depot, Nardelli noticed the stores were staffed with knowledgeable, full-time employees, and in his opinion, a bit too many. What do new leaders, wrongfully, do when they want to make waves and save money?

Yep, he downsized to optimize costs.

He decided to hire more part-timers, many of whom had less expertise in home improvement. The results were not what he expected. Customers quickly noticed the absence of their favorite employees and the decline in service quality. It turned out that managing a service organization like Home Depot was very different from managing a manufacturing operation.

This story underscores a critical point: leading a service organization requires a different approach — one that prioritizes employee engagement and expertise.

“Employees first, customers second” is still about serving the customer, but it’s about serving the customer through the employees whose job it is to serve the customer. Weird how that works, isn’t it? Understand that helping your employees helps your customers. These two parties are intrinsically tied together.

Research On Employees First

Nayar’s success story isn’t an isolated incidence of dumb luck. There’s research behind this. Researchers at Harvard University found a link between employee satisfaction and profitability. They took aim at a long-standing assumption in the business world that market share is the primary driver of profitability. If a company can increase market share, the thinking went, it will increase sales while taking advantage of economies of scale to lower costs and thus increase profits.

However, when they examined a variety of companies and the existing research, they found that market share is one factor in profitability. But that another factor better explains the most profitable companies: customer loyalty.

Based on their research, they estimated that a mere 5 percent increase in customer loyalty can yield a 25 to 85 percent increase in profitability.

Here’s how it works in practice: Profits are driven by customer loyalty. Customer loyalty is driven by employee satisfaction. And employee satisfaction is driven by putting employees first. They called this The Service-Profit Chain and managers who understand this can create a thriving cycle where employee and customer satisfaction drive each other, ultimately leading to greater business success.

In simple terms, if your business provides a service that your employees have front-line participation in, they are in essence an embodiment of the company, not you or the CEO. The entire brand, the experience, the service rests on those front-line employees. If they aren’t taken care of — if they aren’t satisfied — the customer tends to notice.

How Employees First Creates Customer Loyalty

Employee loyalty is a deep indicator of future performance for service organizations. It’s worth noting that there is a subtle difference between employee satisfaction and employee loyalty. Satisfaction derives from how happy employees are in their role. Loyalty comes from having a real stake in the success of the business. Without loyalty, employees leave for better opportunities, then high turnover rates drive up recruitment and training costs, disrupt productivity, and can negatively impact customer experiences. When employees stay longer, companies save on hiring costs, maintain productivity gains, and create a more positive environment for customers.

Simply put, loyal employees lead to loyal customers.

Great service leaders recognize that improving employee retention involves providing opportunities for growth and advancement. This approach keeps talented employees closer to the customer for longer periods, which directly impacts customer satisfaction and loyalty.

Take Whole Foods Market, for example. They have crafted their entire system — from their rigorous selection process to compensation methods — to encourage front-line employees to stay and thrive. Teams at Whole Foods are responsible for setting key metrics, making decisions on how to meet these targets, and even choosing what food items to buy locally. They’re rewarded with bonuses based on team performance, which often includes finding creative ways to boost sales to balance out labor costs. After three years on the job, employees receive stock options, which further incentivizes them to stay.

Additionally, Whole Foods allows employees to vote every three years on various aspects of the benefits package, from community service pay to health insurance provisions. All these factors contribute to Whole Foods’ remarkably low turnover rate of less than 10 percent for full-time employees after the probationary period — far below the industry average.

The results speak for themselves: Whole Foods is regularly rated as one of the best places to work, known for excellent customer service, and boasts some of the highest profits per square foot in the grocery retail industry.

This success is a testament to the power of employee loyalty in driving exceptional service. Great service leadership isn’t just about managing day-to-day operations — it’s about creating an environment where employees feel valued, empowered, and committed. By focusing on employee loyalty, service leaders can build stronger customer relationships and achieve sustainable success.

Employees First For All Leaders

You may not have the power in your organization to completely flip the hierarchy. But there’s still an important lesson for leaders at all levels: Flip the accountability. This can look like bringing in more feedback from front-line employees or just seeing the structure of your team differently. You work for your team. Don’t squeeze your team; foster them to do well.

In addition, give your employees real stakes and invest in them. Prioritize training and growth opportunities for your employees so they know you’re committed to not just their output, but their career. Parties, gift certificates, awards, summer Fridays, bonuses — all of these are great. Do those things. But those are more employee appreciation, not real development. Development looks like sending your rising stars to conferences, workshops, night school even, if you have the budget. Things you think will help them grow as employees, spark innovation, and create future leaders.

Conclusion

If I could put a message on a billboard in front of every Fortune 500 company, it would be this:

People don’t work for you.

Smart leaders know that employees work with them, and ultimately, leaders work for their people. Embracing the “employees first, customers second” philosophy means prioritizing the well-being and growth of employees, enabling them to deliver outstanding service. Happy, engaged employees create satisfied customers. When leaders invest in their teams’ success and happiness, they cultivate a culture where customers feel valued, leading to long-term loyalty and a thriving business.

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Image credit: David Burkus

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Bridging the Gap Between Strategic Ambition and Innovation Delivery

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Why Long-Range Planning and Product Development Rarely Align — And What Companies Can Do About It

Bridging the Gap Between Strategic Ambition and Innovation Delivery

GUEST POST from Noel Sobelman

Across industries, executive teams craft long-range plans (LRPs) with confident projections for revenue growth, market expansion, and innovation impact. But when it comes time to deliver, product development pipelines often tell a different story. This misalignment, between the top-down assumptions embedded in strategic plans and the bottom-up reality of new product development (NPD), is one of the most persistent and under-addressed risks in corporate planning.

The consequences are serious: growth targets are missed, credibility erodes, and shareholder confidence wanes. And yet, many organizations continue to treat this disconnect as inevitable, rather than solvable.

The Illusion of Alignment

On paper, LRPs typically assign a portion of future revenue to innovation — new products, new markets, new business models. This makes sense. In competitive, fast-moving sectors, sustaining growth depends on a constant stream of successful launches.

But few companies take the next step: validating whether their actual innovation pipeline supports those ambitions. The top-down LRP rarely connects meaningfully with the bottom-up details of project timelines, product margins, development risks, or resource constraints.

Leadership may assume, for instance, that new product contributions will ramp up in years three through five of the plan. Yet the NPD pipeline might only be populated with early-phase projects, with no clear line of sight to commercialization in that time frame. Or worse, it might be filled with low upside sustaining efforts that do little to drive long-term growth.

This isn’t just a data problem — it’s an accountability problem.

A Blind Spot in Strategic Execution

Unlike sales or operations, which are frequently forced to reconcile their contributions to the LRP through tangible metrics and quarterly reviews, product development is often allowed to operate in a parallel universe. Project business cases get approved on a rolling basis, disconnected from aggregate targets. Teams work diligently, but no one steps back to ask: Do the numbers add up?

In many organizations, this analysis is simply never done. When questioned about how the pipeline contributes to the LRP, the answers range from vague optimism (“We’ll figure it out”) to manual workarounds (“We added 5% to last year’s numbers to cover new product upside”).

Such informal planning approaches might have been acceptable in a slower, less competitive world. But in today’s environment, where innovation cycles are compressed, capital is scrutinized, and every function is expected to deliver ROI, they fall short.

Interestingly, other parts of the business, particularly operations, already have a model for how to approach this. Manufacturing teams routinely perform network strategy exercises to determine whether they have the physical capacity to meet future demand. They map projected sales to factory utilization, labor capacity, CapEx, and throughput. If there’s a gap, they create an actionable plan.

Yet in most organizations, this rigor stops at the walls of the plant. There is no equivalent exercise on the R&D side to ask: Do we have the innovation pipeline, product plans, and resources required to meet our revenue commitments? Working with our clients, we’ve seen how powerful it is when this same network strategy logic is applied to product development. The exercise shifts the conversation from hope to confidence, from general intent to measurable plans.

The Case for a Unified Growth Strategy

The path forward requires a more integrated, data-driven approach, a growth strategy that spans both the strategic and executional layers of the business.

At the core is a disciplined feedback loop: reconciling the LRP’s innovation-driven revenue expectations with the actual new product roadmap, resource plan, and market assumptions. This means:

Bottom-up modeling of product-level forecasts (volumes, ASPs, margins, launch dates) that aggregate to a portfolio view of expected revenue. Our benchmarks show that without this discipline, overstatements of new product contributions can widen to 20–40% or more in the outer years of the LRP. Modeling helps identify these gaps early, enabling timely course corrections.
Scenario analysis that tests different mixes of existing and in-development products to identify gaps and prioritize high-leverage opportunities.
Risk adjustment grounded in performance benchmarks and realistic probabilities of technical and commercial success, not wishful thinking. Companies that formalize these assumptions often uncover significant overstatements in expected revenue from early-stage projects.
Cross-functional transparency between R&D, finance, operations, and commercial teams to ensure the entire organization is planning from a shared reality.

Working with our clients, we’ve helped build models that mirror this approach, combining innovation pipeline data, financial assumptions, and market insights into a unified view of expected contribution to growth. The result? Greater visibility into how future revenue will be earned and higher confidence in investment decisions. For some organizations, this alignment has helped redirect 10–15% of R&D spend toward higher-value opportunities without increasing total investment.

In nearly every case, the analysis reveals significant gaps between what leadership believes the innovation engine will deliver and what’s realistically in flight. But once exposed, those gaps become manageable. They become actionable.

This isn’t about punishing innovation teams for uncertainty. It’s about giving them, and the organization, an honest view of what’s likely to be delivered and where targeted adjustments are needed.

Building the Capability (Not Just the Model)

Organizations that do this well don’t just build a single model — they build the capability. They embed portfolio management processes that continually evaluate whether innovation plans are aligned with strategic goals. They invest in tools and talent that can translate project business cases into forward-looking financial impact. And critically, they elevate the conversation from “project selection” to “portfolio impact.”

This approach can also shift the internal conversation away from politics and gut feel, and toward clarity and confidence. CFOs, for example, are increasingly demanding to know what they’re getting for the annual increases in R&D spend. A connected, data-rich view of how new product drives future cash flows goes a long way in strengthening that case. We’ve seen how quickly these conversations mature when companies adopt a planning discipline that brings product development onto the same strategic playing field as operations and sales.

The Strategic Imperative

Ultimately, reconciling innovation with the LRP isn’t a nice-to-have. It’s a fiduciary responsibility. Companies make commitments to their boards and investors based on the assumption that R&D investment will deliver a meaningful share of future growth. When that assumption is built on loosely connected plans and unvalidated forecasts, the entire strategy is at risk.

Bridging that gap can unlock substantial value. In our experience, we see organizations with tightly aligned portfolio and strategy processes outperform their peers by as much as 40% in terms of new product ROI and time-to-market.

The good news? The gap is measurable. The tools, models, and methods to close it exist. What’s often missing is the mandate.

Organizations that seize this opportunity will be better equipped to make confident trade-offs, accelerate high-potential initiatives, and pivot early when plans drift off course. They’ll be able to tell a coherent story, not just about where they want to go, but how they plan to get there.

And that story, told with numbers and backed by action, is what distinguishes companies that plan for growth from those that actually deliver it.

If you’re interested in exploring how to better align your product development plans with long-range strategic goals or want to assess the credibility of your innovation pipeline, we’d be happy to share what we’ve learned from working with companies in similar situations.

Image credits: Pexels

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How Cobots are Humanizing the Factory Floor

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The Collaborative Revolution

LAST UPDATED: October 25, 2025 at 4:33PM
How Cobots are Humanizing the Factory Floor - The Collaborative Revolution

GUEST POST from Art Inteligencia

For decades, industrial automation has been defined by isolation. Traditional robots were caged behind steel barriers, massive, fast, and inherently dangerous to humans. They operated on the principle of replacement, seeking to swap out human labor entirely for speed and precision. But as a thought leader focused on human-centered change and innovation, I see this model as fundamentally outdated. The future of manufacturing, and indeed, all operational environments, is not about replacement — it’s about augmentation.

Enter the Collaborative Robot, or Cobot. These smaller, flexible, and safety-certified machines are the definitive technology driving the next phase of the Industrial Revolution. Unlike their predecessors, Cobots are designed to work alongside human employees without protective caging. They are characterized by their force-sensing capabilities, allowing them to stop instantly upon contact, and their ease of programming, often achieved through simple hand-guiding (or “teaching”). The most profound impact of Cobots is not on the balance sheet, but on the humanization of work, transforming dull, dirty, and dangerous tasks into collaborative, high-value roles. This shift requires leaders to address the initial psychological barrier of automation, re-framing the technology as a partner in productivity and safety.

The Three Pillars of Cobot-Driven Human-Centered Innovation

The true value of Cobots lies in how they enable the three core tenets of modern innovation:

1. Flexibility and Agility: Cobots are highly portable and quick to redeploy. A human worker can repurpose a Cobot for a new task — from picking parts to applying glue — in a matter of hours. This means production lines can adapt to short runs and product customization far faster than large, fixed automation systems, giving businesses the agility required in today’s volatile market.
2. Ergonomic and Safety Improvement: Cobots take on the ergonomically challenging or repetitive tasks that lead to human injury (like repeated lifting, twisting, or precise insertion). By handling the “Four Ds” (Dull, Dirty, Dangerous, and Difficult-to-Ergonomically-Design), they dramatically improve worker health, morale, and long-term retention.
3. Skill Elevation and Mastery: Instead of being relegated to simple assembly, human workers are freed to focus on high-judgment tasks: quality control, complex troubleshooting, system management, and, crucially, Cobot programming and supervision. This elevates the entire workforce, shifting roles from manual labor to process management and robot literacy.

“Cobots are the innovation that tells human workers: ‘We value your brain and your judgment, not just your back.’ The factory floor is becoming a collaborative workspace, not a cage, but leaders must proactively communicate the upskilling opportunity.”

Case Study 1: Transforming Aerospace Assembly with Human-Robot Teams

The Challenge:

A major aerospace manufacturer faced significant challenges in the final assembly stage of large aircraft components. Tasks involved repetitive drilling and fastener application in tight, ergonomically challenging spaces. The precision required meant workers were often in awkward positions for extended periods, leading to fatigue, potential errors, and high rates of Musculoskeletal Disorders (MSDs).

The Cobot Solution:

The company deployed a fleet of UR-style Cobots equipped with vision systems. The human worker now performs the initial high-judgment setup — identifying the part and initiating the sequence. The Cobot then precisely handles the heavy, repetitive drilling and fastener insertion. The human worker remains directly alongside the Cobot, performing simultaneous quality checks and handling tasks that require tactile feedback or complex dexterity (like cable routing).

The Innovation Impact:

The process yielded a 30% reduction in assembly time and, critically, a near-zero rate of MSDs related to the process. The human role shifted entirely from physical exertion to supervision and quality assurance, turning an exhausting, injury-prone role into a highly skilled, collaborative function. This demonstrates Cobots’ power to improve both efficiency and human well-being, increasing overall job satisfaction.

Case Study 2: Flexible Automation in Small-to-Medium Enterprises (SMEs)

The Challenge:

A small, family-owned metal fabrication business needed to increase production to meet demand for specialized parts. Traditional industrial robotics were too expensive, too large, and required complex, fixed programming — an impossible investment given their frequent product changeovers and limited engineering staff.

The Cobot Solution:

They invested in a single, affordable, lightweight Cobot (e.g., a FANUC CR series) and installed it on a mobile cart. The Cobot was tasked with machine tending — loading and unloading parts from a CNC machine, a task that previously required a dedicated, monotonous human shift. Because the Cobot could be programmed by simple hand-guiding and a user-friendly interface, existing line workers were trained to set up and manage the robot in under a day, focusing on Human-Robot Interaction (HRI) best practices.

The Innovation Impact:

The Cobot enabled lights-out operation for the single CNC machine, freeing up human workers to focus on higher-value tasks like complex welding, custom finishing, and customer consultation. This single unit increased the company’s throughput by 40% without increasing floor space or headcount. More importantly, it democratized automation, proving that Cobots are the essential innovation that makes high-level automation accessible and profitable for small businesses, securing their future competitiveness.

Companies and Startups to Watch in the Cobot Space

The market is defined by both established players leveraging their industrial expertise and nimble startups pushing the envelope on human-AI collaboration. Universal Robots (UR) remains the dominant market leader, largely credited with pioneering the field and setting the standard for user-friendliness and safety. They are focused on expanding their software ecosystem to make deployment even simpler. FANUC and ABB are the industrial giants who have quickly integrated Cobots into their massive automation portfolios, offering hybrid solutions for high-mix, low-volume production. Among the startups, keep an eye on companies specializing in advanced tactile sensing and vision — the critical technologies that will allow Cobots to handle true dexterity. Companies focusing on AI-driven programming (where the Cobot learns tasks from human demonstration) and mobile manipulation (Cobots mounted on Autonomous Mobile Robots, or AMRs) are defining the next generation of truly collaborative, fully mobile smart workspaces.

The shift to Cobots signals a move toward agile manufacturing and a renewed respect for the human worker. The future factory floor will be a hybrid environment where human judgment, creativity, and problem-solving are amplified, not replaced, by safe, intelligent robotic partners. Leaders who fail to see the Cobot as a tool for human-centered upskilling and empowerment will be left behind in the race for true productivity and innovation. The investment must be as much in robot literacy as it is in the robots themselves.

Disclaimer: This article speculates on the potential future applications of cutting-edge scientific research. While based on current scientific understanding, the practical realization of these concepts may vary in timeline and feasibility and are subject to ongoing research and development.

Image credit: Google Gemini

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Picking Innovation Projects in Four Questions or Less

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Picking Innovation Projects in Four Questions or Less

GUEST POST from Mike Shipulski

It’s a challenge to prioritize and choose innovation projects. There are open questions on the technology, the product/service, the customer, the price and sales volume. Other than that, things are pretty well defined.

But with all that, you’ve still go to choose. Here are four questions that may help in your selection process:

1. Is it big enough?

The project will be long, expensive and difficult. And if the potential increase in sales is not big enough, the project is not worth starting. Think (Price – Cost) x Volume. Define a minimum viable increase in sales and bound it in time. For example, the minimum incremental sales is twenty five million dollars after five years in the market. If the project does not have the potential to meet those criteria, don’t do the project. The difficult question – How to estimate the incremental sales five years after launch? The difficult answer – Use your best judgement to estimate sales based on market size and review your assumptions and predictions with seasoned people you trust.

2. Why you?

High growth markets/applications are attractive to everyone, including the big players and the well-funded start-ups. How does your company have an advantage over these tough competitors? What about your company sets you apart? Why will customers buy from you? If you don’t have good answers, don’t start the project. Instead, hold the work hostage and take the time to come up with good answers. If you come up with good answers, try to answer the next questions. If you don’t, choose another project.

3. How is it different?

If the new technology can’t distinguish itself over existing alternatives, you don’t have a project worth starting. So, how is your new offering (the one you’re thinking about creating) better than the ones that can be purchased today? What’s the new value to the customer? Or, in the lingo of the day, what is the Distinctive Value Proposition (DVP)? If there’s no DVP, there’s no project. If you’re not sure of the DVP, figure that out before investing in the project. If you have a DVP but aren’t sure it’s good enough, figure out how to test the DVP before bringing the DVP to life.

4. Is it possible?

Usually, this is where everyone starts. But I’ve listed it last, and it seems backward. Would you rather spend a year making it work only to learn no one wants it, or would you rather spend a month to learn the market wants it then a year making it work? If you make it work and no one wants it, you’ve wasted a year. If, before you make it work, you learn no one wants it, you’ve spent a month learning the right thing and you haven’t spent a year working on the wrong thing. It feels unnatural to define the market need before making it work, but though it feels unnatural, it can block resources from working on the wrong projects.

Conclusion

There is no foolproof way to choose the best innovation projects, but these four questions go a long way. Create a one-page template with four sections to ask the questions and capture the answers. The sections without answers define the next work. Define the learning objectives and the learning activities and do the learning. Fill in the missing answers and you’re ready to compare one project to another.

Sort the projects large-to-small by Is it big enough? Then, rank the top three by Why you? and How is it different? Then, for the highest ranked project, do the work to answer Is it possible?

If it’s possible, commercialize. If it’s not, re-sort the remaining projects by Is it big enough? Why you? and How is it different? and learn if It is possible.

Image credit: Pexels

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Are You Getting Your Fair Share of $860 Billion?

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Are You Getting Your Fair Share of $860 Billion?

GUEST POST from Shep Hyken

According to Qualtrics, there is an estimated $860 billion worth of revenue and cost savings available for companies that figure out how to create an improved Customer Experience (CX) using AI to better understand and serve their customers. (That includes $420 billion for B2B and $440 billion for B2C.) Qualtrics recently released these figures in a report/eBook titled Unlock the Potential through AI-Enabled CX.

I had a chance to interview Isabelle Zdatny, head of thought leadership at Qualtrics Experience Management Institute, for Amazing Business Radio. She shared insights from the report, including ways in which AI is reshaping how organizations measure, understand and improve their relationships with customers. These ideas are what will help you get more customers, keep existing customers and improve your processes, giving you a share of the $860 billion that is up for grabs. Here are some of the top takeaways from our interview.

AI-Enabled CX Represents a Financial Opportunity

The way AI is used in customer experience is much more than just a way to deflect customers’ questions and complaints to an AI-fueled chatbot or other self-service solution. Qualtrics’ report findings show that the value comes through increased employee productivity, process improvement and revenue growth. Zdatny notes a gap between leadership’s recognition of AI’s potential and their readiness to lead and make a change. Early adopters will likely capture “compounding advantages,” as every customer interaction makes their systems smarter and their advantage more difficult for competitors to overcome. My response to this is that if you aren’t on board with AI for the many opportunities it creates, you’re not only going to be playing catch-up with your competitors, but also having to catch up with the market share you’re losing.

Customers Want Convenience

While overall CX quality is improving, thanks to innovation, today’s customers have less tolerance for friction and mistakes. A single bad experience can cause customers to defect. My customer experience research says an average customer will give you two chances. Zdatny says, “Customers are less tolerant of friction these days. … Deliver one bad experience, and that sends the relationship down a bad path more quickly than it used to.”

AI Takes Us Beyond Surveys

Customer satisfaction surveys can frustrate customers. AI collects the data from interactions between customers and the company and analyzes it using natural language processing and sentiment. It can predict churn and tension. It analyzes customer behavior, and while it doesn’t look at a specific customer (although it can), it is able to spot trends in problems, opportunities and more. The company that uses this information the right way can reap huge financial rewards by creating a better customer experience.

Agentic AI

Agentic AI takes customer interactions to a new level. As a customer interacts with AI-fueled self-service support, the system can do more than give customers information and analyze the interaction. It can also take appropriate action. This is a huge opportunity to make it easier on the workforce as AI processes action items that employees might otherwise handle manually. Think about the dollars saved (part of the $860 billion) by having AI support part of the process so people don’t have to.

Customer Loyalty is at Risk

To wrap this up, Zdatny and I talked about the concept of customer loyalty and how vulnerable companies are to losing their most loyal customers. According to Zdatny, a key reason is the number of options available to consumers. (While there may be fewer options in the B2B world, the concern should still be the same.) Switching brands is easy, and customers are more finicky than ever. Our CX research finds that typical customers give you a second chance before they switch. A loyal customer will give you a third chance — but to put it in baseball terms, “Three strikes and you’re out!” Manage the experience right the first time, and keep in mind that whatever interaction you’re having at that moment is the reason customers will come back—or not—to buy whatever you sell.

Image Credits: Pexels

This article was originally published on Forbes.com

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The Agentic Browser Wars Have Begun

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LAST UPDATED: October 22, 2025 at 9:11AM

The Agentic Browser Wars Have Begun

GUEST POST from Art Inteligencia

On his way out of town to Nashville for Customer Contact Week (CCW) I managed to catch the ear of Braden Kelley (follow him on LinkedIn) to discuss the news that OpenAI is launching its own “agentic” web browser, something that neither of us saw coming given their multi-billion dollar partnership with Microsoft on Copilot. He had some interesting perspectives to share that prompted me to explore the future of the web browser. I hope you enjoy this article (and its embedded videos) on the growing integration of AI into our browsing experiences!

For decades, the web browser has been our window to the digital world — a passive tool that simply displays information. We, the users, have been the active agents, navigating tabs, clicking links, and manually synthesizing data. But a profound shift is underway. The era of the “Agentic Browser” is dawning, and with it, a new battle for the soul of our digital experience. This isn’t just about faster rendering or new privacy features; it’s about embedding proactive, intelligent agents directly into the browser to fundamentally change how we interact with the internet. As a human-centered change and innovation thought leader, I see this as the most significant evolution of the browser since its inception, with massive implications for productivity, information access, and ultimately, our relationship with technology. The Browser Wars 2.0 aren’t about standards; they’re about autonomy.

The core promise of the Agentic Browser is to move from a pull model (we pull information) to a push model (intelligence pushes relevant actions and insights to us). These AI agents, integrated into the browser’s fabric, can observe our intent, learn our preferences, and execute complex, multi-step tasks across websites autonomously. Imagine a browser that doesn’t just show you flight prices, but books your ideal trip, handling preferences, loyalty points, and calendar integration. This isn’t futuristic fantasy; it’s the new battleground, and the titans of tech are already drawing their lines, vying for control over our digital workflow and attention economy.

The Shift: From Passive Viewer to Active Partner

The Agentic Browser represents a paradigm leap. Traditional browsers operate at the rendering layer; Agentic Browsers will operate at the intent layer. They understand why you are on a page, what you are trying to achieve, and can proactively take steps to help you. This requires:

Deep Contextual Understanding: Beyond keywords, the agent understands the semantic meaning of pages and user queries, across tabs and sessions.
Multi-Step Task Execution: The ability to automate a sequence of actions across different domains (e.g., finding information on one site, comparing on another, completing a form on a third). This is the leap from macro automation to intelligent workflow orchestration.
Personalized Learning: Agents learn from user feedback and preferences, refining their autonomy and effectiveness over time, making them truly personal co-pilots.
Ethical and Safety Guardrails: Crucially, these agents must operate with transparent consent, robust safeguards, and clear audit trails to prevent misuse or unintended consequences. This builds the foundational trust architecture.

“The Agentic Browser isn’t just a smarter window; it’s an intelligent co-pilot, transforming the internet from a library into a laboratory where your intentions are actively fulfilled. This is where competitive advantage will be forged.” — Braden Kelley

Case Study 1: OpenAI’s Atlas Browser – A New Frontier, Redefining the Default

The Anticipated Innovation:

While still emerging, reports suggest OpenAI’s foray into the browser space with ‘Atlas‘ (a rumored codename that became real) aims to redefine web interaction. Unlike existing browsers that integrate AI as an add-on, Atlas is expected to have generative AI and autonomous agents at its core. This isn’t just a chatbot in your browser; it’s the browser itself becoming an agent, fundamentally challenging the definition of a web session.

The Agentic Vision:

Atlas could seamlessly perform tasks like:

Dynamic Information Synthesis: Instead of listing search results, it could directly answer complex questions by browsing, synthesizing, and summarizing information across multiple sources, presenting a coherent answer — effectively replacing the manual search-and-sift paradigm.
Automated Research & Comparison: A user asking “What’s the best noise-canceling headphone for long flights under $300?” wouldn’t get links; they’d get a concise report, comparative table, and perhaps even a personalized recommendation based on their past purchase history and stated preferences, dramatically reducing decision fatigue.
Proactive Task Completion: If you’re on a travel site, Atlas might identify your upcoming calendar event and proactively suggest hotels near your conference location, or even manage the booking process with minimal input, turning intent into seamless execution.

The Implications for the Wars:

If successful, Atlas could significantly reduce the cognitive load of web interaction, making information access more efficient and task completion more automated. It pushes the boundaries of how much the browser knows and does on your behalf, potentially challenging the existing search, content consumption, and even advertising models that underpin the current internet economy. This represents a bold, ground-up approach to seizing the future of internet interaction.

Case Study 2: Google Gemini and Chrome – The Incumbent’s Agentic Play

The Incumbent’s Response:

Google, with its dominant Chrome browser and powerful Gemini AI model, is uniquely positioned to integrate agentic capabilities. Their strategy seems to be more iterative, building AI into existing products rather than launching a completely new browser from scratch (though they could). This is a play for ecosystem lock-in and leveraging existing market share.

Current and Emerging Agentic Features:

Google’s approach is visible through features like:

Gemini in Workspace Integration: Already, Gemini can draft emails, summarize documents, and generate content within Google Workspace. Extending this capability directly into Chrome means the browser could understand a tab’s content and offer to summarize it, extract key data, or generate follow-up actions (e.g., “Draft an email to this vendor summarizing their pricing proposal”), transforming Chrome into an active productivity hub.
Enhanced Shopping & Productivity: Chrome’s existing shopping features, when supercharged with Gemini, could become truly agentic. Imagine asking the browser, “Find me a pair of running shoes like these, but with better arch support, on sale.” Gemini could then browse multiple retailers, apply filters, compare reviews, and present tailored options, potentially even initiating a purchase, fundamentally reshaping e-commerce pathways.
Contextual Browsing Assistants: Future iterations could see Gemini acting as a dynamic tutor or research assistant. On a complex technical page, it might offer to explain jargon, find related academic papers, or even help you debug code snippets you’re viewing in a web IDE, creating a personalized learning environment.

The Implications for the Wars:

Google’s strategy is about leveraging its vast ecosystem and existing user base. By making Chrome an agentic hub for Gemini, they can offer seamless, context-aware assistance across search, content consumption, and productivity. The challenge will be balancing powerful automation with user control and data privacy — a tightrope walk for any company dealing with such immense data, and a key battleground for user trust and regulatory scrutiny. Other players like Microsoft (Copilot in Edge) are making similar moves, indicating a clear direction for the entire browser market and intensifying the competitive pressure.

Case Study 3: Microsoft Edge and Copilot – An Incumbent’s Agentic Strategy

The Incumbent’s Response:

Microsoft is not merely a spectator in the nascent Agentic Browser Wars; it’s a significant player, leveraging its robust Copilot AI and the omnipresence of its Edge browser. Their strategy centers on deeply integrating generative AI into the browsing experience, transforming Edge from a content viewer into a dynamic, proactive assistant.

A prime example of this is the “Ask Copilot” feature directly embedded into Edge’s address bar. This isn’t just a search box; it’s an intelligent entry point where users can pose complex queries, ask for summaries of the page they’re currently viewing, compare products from different tabs, or even generate content based on their browsing context. By making Copilot instantly accessible and context-aware, Microsoft aims to make Edge the default browser for intelligent assistance, enabling users to move beyond manual navigation and towards seamless, AI-driven task completion and information synthesis without ever leaving their browser.

The Human-Centered Imperative: Control, Trust, and the Future of Work

As these Agentic Browsers evolve, the human-centered imperative is paramount. We must ensure that users retain control, understand how their data is being used, and can trust the agents acting on their behalf. The future of the internet isn’t just about more intelligence; it’s about more empowered human intelligence. The browser wars of the past were about speed and features. The Agentic Browser Wars will be fought on the battleground of trust, utility, and seamless human-AI collaboration, fundamentally altering our digital workflows and requiring us to adapt.

For businesses, this means rethinking your digital presence: How will your website interact with agents? Are your services agent-friendly? For individuals, it means cultivating a new level of digital literacy: understanding how to delegate tasks, verify agent output, and guard your privacy in an increasingly autonomous online world. The passive web is dead. Long live the agentic web. The question is, are you ready to engage in the fight for its future?

Image credit: Gemini

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How Tangible AI Artifacts Accelerate Learning and Alignment

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Seeing the Invisible

By Douglas Ferguson, Founder & CEO of Voltage Control
Originally inspired by “A Lantern in the Fog” on Voltage Control, where teams learn to elevate their ways of working through facilitation mastery and AI-enabled collaboration.

Innovation isn’t just about generating ideas — it’s about testing assumptions before they quietly derail your progress. The faster a team can get something tangible in front of real eyes and minds, the faster they can learn what works, what doesn’t, and why.

Yet many teams stay stuck in abstraction for too long. They debate concepts before they draft them, reason about hypotheses before they visualize them, and lose energy to endless interpretation loops. That’s where AI, when applied strategically, becomes a powerful ally in human-centered innovation — not as a shortcut, but as a clarifier.

How Tangible AI Artifacts Accelerate Learning and Alignment

At Voltage Control, we’ve been experimenting with a practice we call AI Teaming — bringing AI into the collaborative process as a visible, participatory teammate. Using new features in Miro, like AI Flows and Sidekicks, we’re able to layer prompts in sequence so that teams move from research to prototypes in minutes. We call this approach Instant Prototyping — because the prototype isn’t the end goal. It’s the beginning of the real conversation.

Tangibility Fuels Alignment

In human-centered design, the first artifact is often the first alignment. When a team sees a draft — even one that’s flawed — it changes how they think and talk. Suddenly, discussions move from “what if” to “what now.” That’s the tangible magic: the moment ambiguity becomes visible enough to react to.

AI can now accelerate that moment. With one-click flows in Miro, facilitators can generate structured artifacts — such as user flows, screen requirements, or product briefs — based on real research inputs. The output isn’t meant to be perfect; it’s meant to be provocative. A flawed draft surfaces hidden assumptions faster than another round of theorizing ever could.

Each iteration reveals new learning: the missing user story, the poorly defined need, the contradiction in the strategy. These insights aren’t AI’s achievement — they’re the team’s. The AI simply provides a lantern, lighting up the fog so humans can decide where to go next.

Layering Prompts for Better Hypothesis Testing

One of the most powerful aspects of Miro’s new AI Flows is the ability to layer prompts in connected sequences. Instead of a single one-off query, you create a chain of generative steps that build on each other. For example:

Synthesize research into user insights.
Translate insights into “How Might We” statements.
Generate user flows based on selected opportunities.
Draft prototype screens or feature lists.

Each layer of the flow uses the prior outputs as inputs — so when you adjust one, the rest evolves. Change a research insight or tweak your “How Might We” framing, and within seconds, your entire prototype ecosystem updates. It’s an elegant way to make hypothesis testing iterative, dynamic, and evidence-driven.

Seeing the Invisible

In traditional innovation cycles, these transitions can take weeks of hand-offs. With AI flows, they happen in minutes — creating immediate feedback loops that invite teams to think in public and react in real time.

(You can see this process in action in the video embedded below — where we walk through how small prompt adjustments yield dramatically different outputs.)

The Human Element: Facilitating Sensemaking

The irony of AI-assisted innovation is that the faster machines generate, the more valuable human facilitation becomes. Instant prototypes don’t replace discussion — they accelerate it. They make reflection, critique, and sensemaking more productive because there’s something concrete to reference.

Facilitators play a critical role here. Their job is to:

Name the decision up front: “By the end of this session, we’ll have a directionally correct concept we’re ready to test.”
Guide feedback: Ask, “What’s useful? What’s missing? What will we try next?”
Anchor evidence: Trace changes to specific research insights so teams stay grounded.
Enable iteration: Encourage re-running the flow after prompt updates to test the effect of new assumptions.

Through this rhythm of generation, reflection, and adjustment, AI becomes a conversation catalyst — not a black box. And the process stays deeply human-centered because it focuses on learning through doing.

Case in Point: Building “Breakout Buddy”

We recently used this exact approach to prototype a new tool called Breakout Buddy — a Zoom app designed to make virtual breakout rooms easier for facilitators. The problem was well-known in our community: facilitators love the connection of small-group moments but dread the logistics. No drag-and-drop, no dynamic reassignment, no simple timers.

Using our Instant Prototyping flow, we gathered real facilitator pain points, synthesized insights, and created an initial app concept in under two hours. The first draft had errors — it misunderstood terms like “preformatted” and missed saving room configurations — but that’s precisely what made it valuable. Those gaps surfaced the assumptions we hadn’t yet defined.

After two quick iterations, we had a working prototype detailed enough for a designer to polish. Within days, we had a testable artifact, a story grounded in user evidence, and a clear set of next steps. The magic wasn’t in the speed — it was in how visible our thinking became.

Designing for Evidence, Not Perfection

If innovation is about learning, then prototypes are your hypotheses made tangible. AI just helps you create more of them — faster — so you can test, compare, and evolve. But the real discipline lies in how you use them.

Don’t rush past the drafts. Study what’s wrong and why.
Don’t hide your versions. Keep early artifacts visible to trace the evolution.
Don’t over-polish. Each iteration should teach, not impress.

When teams treat AI outputs as living evidence rather than final answers, they stay in the human-centered loop — grounded in empathy, focused on context, and oriented toward shared understanding.

A Lantern in the Fog

At Voltage Control, we see AI not as a replacement for creative process, but as a lantern in the fog — illuminating just enough of the path for teams to take their next confident step. Whether you’re redesigning a product, reimagining a service, or exploring cultural transformation, the goal isn’t to hand creativity over to AI. It’s to use AI to make your learning visible faster.

Because once the team can see it, they can improve it. And that’s where innovation truly begins.

🎥 Watch the Demo: How layered AI prompts accelerate hypothesis testing in Miro

Join the waitlist to get your hands on the Instant Prototyping template

Image Credit: Douglas Ferguson, Unsplash

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Why Best Practices Fail

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Five Questions with Ellen DiResta

Why Best Practices Fail

GUEST POST from Robyn Bolton

For decades, we’ve faithfully followed innovation’s best practices. The brainstorming workshops, the customer interviews, and the validated frameworks that make innovation feel systematic and professional. Design thinking sessions, check. Lean startup methodology, check. It’s deeply satisfying, like solving a puzzle where all the pieces fit perfectly.

Problem is, we’re solving the wrong puzzle.

As Ellen Di Resta points out in this conversation, all the frameworks we worship, from brainstorming through business model mapping, are business-building tools, not idea creation tools.

Read on to learn why our failure to act on the fundamental distinction between value creation and value capture causes too many disciplined, process-following teams to create beautiful prototypes for products nobody wants.

Robyn: What’s the one piece of conventional wisdom about innovation that organizations need to unlearn?

Ellen: That the innovation best practices everyone’s obsessed with work for the early stages of innovation.

The early part of the innovation process is all about creating value for the customer. What are their needs? Why are their Jobs to be Done unsatisfied? But very quickly we shift to coming up with an idea, prototyping it, and creating a business plan. We shift to creating value for the business, before we assess whether or not we’ve successfully created value for the customer.

Think about all those innovation best practices. We’ve got business model canvas. That’s about how you create value for the business. Right? We’ve got the incubators, accelerators, lean, lean startup. It’s about creating the startup, which is a business, right? These tools are about creating value for the business, not the customer.

R: You know that Jobs to be Done is a hill I will die on, so I am firmly in the camp that if it doesn’t create value for the customer, it can’t create value for the business. So why do people rush through the process of creating ideas that create customer value?

E: We don’t really teach people how to develop ideas because our culture only values what’s tangible. But an idea is not a tangible thing so it’s hard for people to get their minds around it. What does it mean to work on it? What does it mean to develop it? We need to learn what motivates people’s decision-making.

Prototypes and solutions are much easier to sell to people because you have something tangible that you can show to them, explain, and answer questions about. Then they either say yes or no, and you immediately know if you succeeded or failed.

R: Sounds like it all comes down to how quickly and accurately can I measure outcomes?

E: Exactly. But here’s the rub, they don’t even know they’re rushing because traditional innovation tools give them a sense of progress, even if the progress is wrong.

We’ve all been to a brainstorm session, right? Somebody calls the brainstorm session. Everybody goes. They say any idea is good. Nothing is bad. Come up with wild, crazy ideas. They plaster the walls with 300 ideas, and then everybody leaves, and they feel good and happy and creative, and the poor person who called the brainstorm is stuck.

Now what do they do? They look at these 300 ideas, and they sort them based on things they can measure like how long it’ll take to do or how much money it’ll cost to do it. What happens? They end up choosing the things that we already know how to do! So why have the brainstorm?”

R: This creates a real tension: leadership wants progress they can track, but the early work is inherently unmeasurable. How do you navigate that organizational reality?

E: Those tangible metrics are all about reliability. They make sure you’re doing things right. That you’re doing it the same way every time? And that’s appropriate when you know what you’re doing, know you’re creating value for the customer, and now you’re working to create value for the business. Usually at scale

But the other side of it? That’s where you’re creating new value and you are trying to figure things out. You need validity metrics. Are we doing the right things? How will we know that we’re doing the right things.

R: What’s the most important insight leaders need to understand about early-stage innovation?

E: The one thing that the leader must do is run cover. Their job is to protect the team who’s doing the actual idea development work because that work is fuzzy and doesn’t look like it’s getting anywhere until Ta-Da, it’s done!

They need to strategically communicate and make sure that the leadership hears what they need to hear, so that they know everything is in control, right? And so they’re running cover is the best way to describe it. And if you don’t have that person, it’s really hard to do the idea development work.”

But to do all of that, the leader also must really care about that problem and about understanding the customer.

We must create value for the customer before we can create value for the business. Ellen’s insight that most innovation best practices focus on the latter is devastating. It’s also essential for all the leaders and teams who need results from their innovation investments.

Before your next innovation project touches a single framework, ask yourself Ellen’s fundamental question: “Are we at a stage where we’re creating value for the customer, or the business?” If you can’t answer that clearly, put down the canvas and start having deeper conversations with the people whose problems you think you’re solving.

To learn more about Ellen’s work, check out Pearl Partners.

To dive deeper into Ellen’s though leadership, visit her Substack – Idea Builders Guild.

To break the cycle of using the wrong idea tools, sign-up for her free one-hour workshop.

Image credit: 1 of 950+ FREE quote slides available at http://misterinnovation.com

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Innovation or Not – Chemical-Free Farming with Autonomous Robots

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Greenfield Robotics and the Human-Centered Reboot of Agriculture

LAST UPDATED: October 20, 2025 at 9:35PM
Innovation or Not - Chemical-Free Farming with Autonomous Robots

GUEST POST from Art Inteligencia

The operating system of modern agriculture is failing. We’ve optimized for yield at the cost of health—human health, soil health, and planetary health. The relentless pursuit of chemical solutions has led to an inevitable biological counter-strike: herbicide-resistant superweeds and a spiraling input cost crisis. We’ve hit the wall of chemical dependency, and the system is demanding a reboot.

This is where the story of Greenfield Robotics — a quiet, powerful disruption born out of a personal tragedy and a regenerative ethos—begins to rewrite the agricultural playbook. Founded by third-generation farmer Clint Brauer, their mission isn’t just to sell a better tool; it’s to eliminate chemicals from our food supply entirely. This is the essence of true, human-centered innovation: identifying a catastrophic systemic failure and providing an elegantly simple, autonomous solution.

The Geometry of Disruption: From Spray to Scalpel

For decades, weed control has been a brute-force exercise. Farmers apply massive spray rigs, blanketing fields with chemicals to kill the unwanted. This approach is inefficient, environmentally harmful, and, critically, losing the biological war.

Greenfield Robotics flips this model from a chemical mass application to a mechanical, autonomous precision action. Their fleet of small, AI-powered robots—the “Weedbots” or BOTONY fleet—are less like tractors and more like sophisticated surgical instruments. They are autonomous, modular, and relentless.

Imagine a swarm of yellow, battery-powered devices, roughly two feet wide, moving through vast crop rows 18 hours a day, day or night. This isn’t mere automation; it’s coordinated, intelligent fleet management. Using proprietary AI-powered machine vision, the bots navigate with centimeter accuracy, identifying the crop from the weed. Their primary weapon is not a toxic spray, but a spinning blade that mechanically scalps the ground, severing the weed right at the root, ensuring chemical-free eradication.

This seemingly simple mechanical action represents a quantum leap in agricultural efficiency. By replacing chemical inputs with a service-based autonomous fleet, Greenfield solves three concurrent crises:

Biological Resistance: Superweeds cannot develop resistance to being physically cut down.
Environmental Impact: Zero herbicide use means zero chemical runoff, protecting water systems and beneficial insects.
Operational Efficiency: The fleet runs continuously and autonomously (up to 1.6 meters per second), drastically increasing the speed of action during critical growth windows and reducing the reliance on increasingly scarce farm labor.

The initial success is staggering. Working across broadacre crops like soybeans, cotton, and sweet corn, farmers are reporting higher yields and lower costs comparable to, or even better than, traditional chemical methods. The economic pitch is the first step, but the deeper change is the regenerative opportunity it unlocks.

The Human-Centered Harvest: Regenerative Agriculture at Scale

As an innovation leader, I look for technologies that don’t just optimize a process, but fundamentally elevate the human condition around that process. Greenfield Robotics is a powerful example of this.

The human-centered core of this innovation is twofold: the farmer and the consumer.

For the farmer, this technology is an act of empowerment. It removes the existential dread of mounting input costs and the stress of battling resistant weeds with diminishing returns. More poignantly, it addresses the long-term health concerns associated with chemical exposure—a mission deeply personal to Brauer, whose father’s Parkinson’s diagnosis fueled the company’s genesis. This is a profound shift: A technology designed to protect the very people who feed the world.

Furthermore, the modular chassis of the Weedbot is the foundation for an entirely new Agri-Ecosystem Platform. The robot is not limited to cutting weeds. It can be equipped to:

Plant cover crops in-season.
Apply targeted nutrients, like sea kelp, with surgical precision.
Act as a mobile sensor platform, collecting data on crop nutrient deficiencies to guide farmer decision-making.

This capability transforms the farmer’s role from a chemical applicator to a regenerative data strategist. The focus shifts from fighting nature to working with it, utilizing practices that build soil health—reduced tillage, increased biodiversity, and water retention. The human element moves up the value chain, focused on strategic field management powered by real-time autonomous data, while the robot handles the tireless, repeatable, physical labor.

For the consumer, the benefit is clear: chemical-free food at scale. The investment from supply chain giants like Chipotle, through their Cultivate Next venture fund, is a validation of this consumer-driven imperative. They understand that meeting the demand for cleaner, healthier food requires a fundamental, scalable change in production methods. Greenfield provides the industrialized backbone for regenerative, herbicide-free farming—moving this practice from niche to normalized.

Beyond the Bot: A Mindset for Tomorrow’s Food System

The challenge for Greenfield Robotics, and any truly disruptive innovator, is not the technology itself, but the organizational and cultural change required for mass adoption. We are talking about replacing a half-century-old paradigm of chemical dependency with an autonomous, mechanical model. This requires more than just selling a machine; it requires cultivating a Mindset Shift in the farming community.

The company’s initial “Robotics as a Service” model was a brilliant, human-centered strategy for adoption. By deploying, operating, and maintaining the fleets themselves for a per-acre fee, they lowered the financial and technical risk for farmers. This reduced-friction introduction proves that the best innovation is often wrapped in the most accessible business model. As the technology matures, transitioning toward a purchase/lease model shows the market confidence and maturity necessary for exponential growth.

Greenfield Robotics is more than a promising startup; it is a signal. It tells us that the future of food is autonomous, chemical-free, and profoundly human-centered. The next chapter of agriculture will be written not with larger, more powerful tractors and sprayers, but with smaller, smarter, and more numerous robots that quietly tend the soil, remove the toxins, and enable the regenerative practices necessary for a sustainable, profitable future.

This autonomous awakening is our chance to heal the rift between technology and nature, and in doing so, secure a healthier, cleaner food supply for the next generation. The future of farming is not just about growing food; it’s about growing change.

Image credit: Greenfield Robotics

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Moving From Disruption to Resilience

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Moving From Disruption To Resilience

GUEST POST from Greg Satell

In the 1990s, a newly minted professor at Harvard Business School named Clayton Christensen began studying why good companies fail. What he found was surprising. They weren’t failing because they lost their way, but rather because they were following time-honored principles, such as listening to their customers, investing in R&D and improving their products.

As he researched further he realized that, under certain circumstances, a market becomes over-served, the basis of competition changes and firms become vulnerable to a new type of competitor. In his 1997 book, The Innovator’s Dilemma, he coined the term disruptive technology.

It was an idea whose time had come. The book became a major bestseller and Christensen the world’s top business guru. Yet many began to see disruption as more than a special case, but a mantra; an end in itself rather than a means to an end. Today, we’ve disrupted ourselves into oblivion and we desperately need to make a shift. It’s time to move toward resilience.

The Disruption Gospel

We like to think of ourselves as living in a fast-moving age, but that’s probably more hype than anything else. Before 1920 most households in America lacked electricity and running water. Even the most basic household tasks, like washing or cooking a meal, took hours of backbreaking labor to haul water and cut firewood. Cars were rare and few people traveled more than 10 miles from home.

That would change in the next few decades as household appliances and motorized transportation transformed American life. The development of penicillin in the 1940s would bring about a “Golden Age” of antibiotics and revolutionize medicine. The 1950s brought a Green Revolution that would help expand overseas markets for American goods.

By the 1970s, innovation began to slow. After half a century of accelerated productivity growth, it would enter a long slump. The rise of Japan and stagflation contributed to an atmosphere of malaise. After years of dominance, the American model seemed to have its best days behind it. For the first time in the post-war era, the future was uncertain.

That began to change in the 1980s. A new president, Ronald Reagan, talked of a “shining city on a hill”, and declared that “Government is not the solution to our problem, government is the problem.” A new “Washington Consensus,” took hold that preached fiscal discipline, free trade, privatization and deregulation.

At the same time a management religion took hold, with Jack Welch as its patron saint. No longer would CEO’s weigh the interests of investors with customers, communities, employees and other stakeholders, everything would be optimized for shareholder value. General Electric, and then broader industry, would embark on a program of layoffs, offshoring and financial engineering in order to trim the fat and streamline their organizations.

The End Of History?

There were early signs that we were on the wrong path. Despite the layoffs that hollowed out America’s industrial base and impoverished many of its communities, productivity growth, which had been depressed since the 1970s, didn’t even budge. Poorly thought out deregulation in the banking industry led to a savings and loan crisis and a recession.

At this point, questions should have been raised, but two events in November 1989 would reinforce the prevailing wisdom. First, The fall of the Berlin Wall would end the Cold War and discredit socialism. Then Tim Berners-Lee would create the World Wide Web and usher in a new technological era of networked computing.

With markets opening across the world, American-trained economists at the IMF and the World Bank traveled the globe preaching the market discipline prescribed by the Washington Consensus, often imposing policies that would never be accepted developed markets back home. Fueled by digital technology, productivity growth in the US finally began to pick up in 1996, creating budget surpluses for the first time in decades.

Finally, it appeared that we had hit upon a model that worked. We would no longer leave ourselves to the mercy of bureaucrats at government agencies or executives at large organizations who had gotten fat and sloppy. The combination of market and technological forces would point the way for us.

The calls for deregulation increased, even if it meant increased disruption. Most notably, Glass-Steagall Act, which was designed to limit risk in the financial system, was repealed in 1999. Times were good and we had unbridled capitalism and innovation to thank for it. The Washington Consensus had been proven out, or so it seemed.

The Silicon Valley Doomsday Machine

By the year 2000, the first signs of trouble began to appear. The money rushing into Silicon Valley created a bubble which bursted and took several notable corporations with it. Massive frauds were uncovered at firms like Enron and WorldCom, which also brought down their auditor, Arthur Anderson. Calls for reform led to the Sarbanes-Oxley Act that increased standards for corporate governance.

Yet the Bush Administration concluded that the problem was too little disruption, not too much, and continued to push for less regulation. By 2005, the increase in productivity growth that began in 1996 dissipated as suddenly as it had appeared. Much like in the late 80s, the lack of oversight led to a banking crisis, except this time it wasn’t just regional savings and loans that got caught up, but the major financial center institutions left exposed.

That’s what led to the Great Recession. To stave off disaster, central banks embarked on an extremely stimulative strategy called quantitative easing. This created a superabundance of capital which, with few places to go, ended up sloshing around in Silicon Valley helping to create a new age of “unicorns,” with over 1000 startups valued at more than $1 billion.

Today, we’re seeing the same kind of scandals we saw in the early 2000’s, except the companies being exposed aren’t established firms like Enron, Worldcom and Arthur Anderson, but would-be disrupters like WeWork, Theranos and FTX. Unlike those earlier failures, there has been no reckoning. If anything, tech billionaires like Marc Andreessen and Elon Musk billionaires seem emboldened.

At the same time, there is growing evidence that hyped-up excesses are crowding out otherwise viable businesses in the real economy. When WeWork “disrupted” other workspaces it wasn’t because of any innovation, technological or otherwise, but rather because huge amounts of venture capital allowed it to undercut competitors. Silicon Valley is beginning to look less like an industry paragon and more like a doomsday machine.

Realigning Prosperity With Security

It’s been roughly 25 years since Clayton Christensen inaugurated the disruptive era and what he initially intended to describe as a special case has been implemented as a general rule. Disruption is increasingly self-referential, used as both premise and conclusion, while the status quo is assumed to be inadequate as an a priori principle.

The results, by just about any metric imaginable, have been tragic. Despite all the hype about innovation, productivity growth remains depressed. Two decades of lax antitrust enforcement have undermined competitive markets in the US. We’ve gone through the worst economic crisis since the 1930s and the worst pandemic since the 1910s.

At the same time, social mobility is declining, while anxiety and depression are rising to epidemic levels. Wages have stagnated, while the cost of healthcare and education has soared. Income inequality is at its highest level in 50 years. The average American is worse off, in almost every way, than before the cult of disruption took hold.

It doesn’t have to be this way. We can change course and invest in resilience. There have been positive moves. The infrastructure legislation and the CHIPS legislation both represent huge investments in our future, while the poorly named Inflation Reduction Act represents the largest investment in climate ever. Businesses have begun reevaluating their supply chains.

Yet the most important shift, that of mindset, has yet to come. Not everything needs to be optimized. Not every cost needs to be cut. We cannot embark on changes just for change’s sake. We need to pursue fewer initiatives that achieve greater impact and, when we feel the urge to disrupt, we need to ask, disruption in the service of what?

— Article courtesy of the Digital Tonto blog
— Image credit: Pixabay

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